Top Data Engineer Jobs in Raleigh, NC

Build and maintain data pipelines integrating laboratory instruments, automation systems, operational platforms, APIs, robotics, databases, and files. Develop SQL transformations, data models, validation checks, orchestration, monitoring, and alerting to support analytics, dashboards, troubleshooting, and AI systems. Document infrastructure and datasets for regulatory compliance while collaborating with engineering, laboratory operations, data science, and automation teams. This is an on-site role with on-call and occasional weekend or holiday support.

Provide technical leadership for analytics engineering across enterprise platforms. Design and optimize scalable Databricks data pipelines using Python, PySpark, and SQL; develop governed Power BI solutions, semantic models, dashboards, and executive reporting. Partner with stakeholders to define metrics, deliver AI-enabled insights, and improve decision-making. Ensure data quality, lineage, security, and governance while optimizing models, queries, and pipelines. Lead large-scale technical initiatives, influence architecture, and guide analytics engineers.

Develop and implement data solutions, pipelines, architectures, analytics, reports, and dashboards for clients. Apply Python, Java, ETL, business intelligence, and advanced analytics techniques to interpret complex data, improve operations, and support strategic decisions. Collaborate with stakeholders, manage client relationships, identify data challenges, and deliver high-quality managed services work while contributing to continuous process improvement.

Design, implement, and manage advanced data ecosystems and scalable ETL/ELT pipelines. Analyze complex problems, deliver data warehousing and governance solutions, mentor junior staff, and build client relationships while maintaining high standards and business-context awareness.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

The Data Engineer supports the data and analytics lifecycle by designing, developing, testing, and maintaining Big Data applications and pipelines. Responsibilities include building Spark and Scala processing logic, optimizing Hive and SQL queries, and maintaining integrations with HBase, Solr, JanusGraph, Kafka, and downstream systems. The role collaborates across business and technology teams, supports solution design and estimation, and develops skills through mentorship.

Build and operate production-grade ELT pipelines from product, Salesforce, and third-party systems into Snowflake using dbt. Ensure testing, monitoring, schema versioning, governance, privacy, access controls, data quality, cost, and performance. Support AI initiatives with trusted data, contribute to warehouse and semantic-layer architecture, and translate cross-functional stakeholder needs into scalable data solutions.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Build, test, monitor, validate, and support scalable ETL/ELT data pipelines and validation frameworks in Azure/Databricks. Perform source-to-target validation, data profiling, reconciliation, anomaly detection, automation, and production support to ensure data quality and reliability for analytics and reporting.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

Configure and maintain Adobe Experience Platform and Real-Time CDP data pipelines, XDM schemas, datasets, identity resolution, Profile enablement, segmentation readiness, and destination activation. Validate data quality, troubleshoot ingestion and identity issues, manage sandboxes, and implement privacy, consent, governance, and access controls. Partner with architects, data engineers, consultants, analysts, data scientists, and marketing teams to support reporting, personalization, and machine learning readiness.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

Design, build, and operate end-to-end ELT data pipelines on Azure/Databricks. Implement DLT data quality checks, CDC, schema evolution, dimensional models for Power BI, monitoring, CI/CD, and operational runbooks while partnering with architects and BI stakeholders.

Design, build, and maintain BI-ready datasets, dashboards, and monitoring tools for high-throughput lab operations. Implement process monitoring (SPC), ensure data quality and audit readiness, perform root-cause analysis, and partner with Data Science to deploy predictive models that improve throughput, quality, and compliance.

Leads enterprise data architecture and engineering initiatives, building scalable cloud data lakes, pipelines, warehouses, and integration frameworks. Designs dimensional models, ETL/ELT patterns, governance practices, and analytics solutions using Snowflake, Azure, AWS, and big data technologies. Oversees testing, QA readiness, documentation, project planning, risk management, and delivery across multiple platforms. Collaborates with business and analytics teams, advises senior leadership, resolves technical issues, and mentors junior engineers.

Design, build, and optimize large-scale Databricks/Spark/Delta Lake data pipelines and Redshift data platforms. Implement ETL/ELT patterns, ingest telemetry and behavioral data (Matomo, FullStory, Pendo), improve performance and observability, provide L2/L3 production support and RCA, and mentor engineers while shaping data architecture and best practices.

Designs and operates batch, streaming, and real-time data platforms and pipelines using Spark, Kafka, Iceberg, Airflow, and cloud infrastructure. Owns data lake evolution, data quality, observability, reliability, governance, event instrumentation, schema management, and privacy controls for PII, retention, deletion, and residency. Leads complex initiatives end-to-end, troubleshoots production issues, mentors engineers, and develops AI agent harnesses that enforce data engineering standards and quality gates.

Designs and implements cloud-first, event-driven data solutions and ETL pipelines for data warehouses (Snowflake/Oracle). Leads architecture, CI/CD, QA, and Agile delivery; mentors teams; builds scalable backend systems using AWS, APIs, and automation tools. Ensures project delivery on time and within budget while advising senior management on technical strategy.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

Designs and manages scalable data pipelines, contracts, transformation layers, and analytics models. The role focuses on data governance, observability, lineage, configuration validation, and collaboration with product engineering teams. It requires processing large datasets, integrating cloud and on-premises industrial technologies, and implementing streaming and message-queuing solutions, ideally in cybersecurity or industrial environments.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Build, deploy, and operate production-grade data pipelines and data products for healthcare audiences. Design transformations, data models, and governed views using Python, SQL, Airflow, S3, Snowflake, and EMR. Implement data-quality, monitoring, and privacy-by-design controls for PHI/PII. Partner with product, analytics, and platform teams to onboard sources, support audience discovery, segmentation, activation, measurement, and troubleshoot production issues.